Syntactic discriminative language model rerankers for statistical machine translation

  • Authors:
  • Simon Carter;Christof Monz

  • Affiliations:
  • ISLA, University of Amsterdam, Amsterdam, The Netherlands 1098 XH;ISLA, University of Amsterdam, Amsterdam, The Netherlands 1098 XH

  • Venue:
  • Machine Translation
  • Year:
  • 2011

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Abstract

This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical Machine Translation output and human translations. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST's MT-Eval benchmarks. While deep features extracted from parse trees do not consistently help, we show how features extracted from a shallow Part-of-Speech annotation layer outperform a competitive baseline and a state-of-the-art comparative reranking approach, leading to significant BLEU improvements on three different test sets.